CN109634801B - Data trend analysis method, system, computer device and readable storage medium - Google Patents
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Abstract
The invention provides a data trend analysis method, a data trend analysis system, a computer device and a computer readable storage medium. The data trend analysis method comprises the following steps: acquiring time sequence data of a monitored object; generating a trend graph corresponding to each monitoring category of the monitored object according to the time sequence data; obtaining extreme points contained in each trend graph through statistics of a preset trend analysis algorithm; judging whether the monitoring type corresponding to each trend graph is abnormal or not according to the extreme point of each trend graph; and when the monitoring type is judged to be abnormal, outputting corresponding warning information according to the attribute information of the extreme point of the monitoring type. The invention can realize the analysis and judgment of the running state trend of the monitored object based on the data analysis algorithm, thereby finding the problem in advance and giving an alarm in advance.
Description
Technical Field
The present invention relates to the field of data processing, and in particular, to a data trend analysis method, system, computer device, and computer-readable storage medium.
Background
The automatic monitoring of equipment faults becomes an important technical means for guaranteeing the normal operation of equipment. When a certain parameter of the equipment exceeds a preset alarm threshold value, the equipment can send out corresponding alarm information. However, the equipment monitoring platform cannot analyze and judge the running state trend of the monitored object, so that problems cannot be found in advance, and warning notification cannot be given in advance.
This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims and the detailed description. The description herein is not admitted to be prior art by inclusion in this section.
Disclosure of Invention
In view of the above, the present invention provides a data trend analysis method, system, computer device and computer readable storage medium, which can analyze and determine the device operation state trend in advance to perform early warning.
An embodiment of the present application provides a data trend analysis method, including:
acquiring time sequence data of a monitored object, wherein the monitored object comprises one or more monitoring categories, and the time sequence data is a parameter data set of each monitoring category on different time nodes;
generating a trend graph corresponding to each monitoring category according to the time sequence data;
obtaining extreme points contained in each trend graph through statistics of a preset trend analysis algorithm;
judging whether the monitoring type corresponding to each trend graph is abnormal or not according to the extreme point of each trend graph; and
and when the monitoring type is judged to be abnormal, outputting corresponding warning information according to the attribute information of the extreme point of the monitoring type.
Preferably, the step of generating a trend graph corresponding to each of the monitoring categories from the time-series data comprises:
classifying the time series data according to each monitoring category;
establishing an XY coordinate axis, and taking each time node in the time sequence data of the first monitoring category of the monitored object as a point of the trend graph on the X axis; and
and taking the parameter data corresponding to each time node as the value of the trend graph on the Y axis to obtain the trend graph corresponding to the first monitoring category.
Preferably, the step of statistically deriving the extreme points included in each of the trend graphs by using a preset trend analysis algorithm includes:
randomly selecting time node data and previous time node data adjacent to the time node data from a trend graph;
calculating a trend slope between the time node data and the last time node data;
judging whether the calculated trend slope is larger than a preset threshold value or not; and
and when the trend slope is larger than the preset threshold, judging that the time node data is an extreme point in the trend graph.
Preferably, the trend slope of the time node data and the last time node data may be calculated by the following mathematical formula:
Km＝|(Vm-Vm-1)/(tm-tm-1)|
wherein, KmIs the slope of the trend, tmIs a time node corresponding to the time node data, tm-1Is given asmAdjacent last time node, VmAs time node tmCorresponding parameter data, Vm-1As time node tm-1Corresponding parameter data.
Preferably, the step of determining whether the monitoring category corresponding to the trend graph is abnormal according to the extreme point of each trend graph includes:
randomly selecting an extreme point from a trend graph, and acquiring at least two pieces of previous time node data adjacent to the extreme point, wherein the at least two pieces of previous time node data comprise first time node data and second time node data;
respectively calculating a first trend slope between the extreme point and the first time node data, and a second trend slope between the extreme point and the second time node data, wherein the first time node data is the last time node data adjacent to the extreme point, and the second time node data is the last time node data adjacent to the first time node data;
calculating the standard deviation and the mean slope of the first trend slope and the second trend slope;
calculating to obtain the comprehensive trend slope of the extreme point according to the standard deviation and the mean slope obtained by calculation;
judging whether the comprehensive trend slope of the extreme point is within a preset range value or not; and
and when the comprehensive trend slope of the extreme point is not within the preset range value, judging that the monitoring category corresponding to the trend graph is abnormal.
Preferably, the integrated trend slope of the extreme point can be calculated by the following mathematical formula:
K＝(Km,m-1-Om)/Km,sd*Km,m-1+(Km,m-2-Om)/Km,sd*Km,m-2；
wherein K is the slope of the comprehensive trend, Km,m-1Is the first trend slope, Km,m-2Is the slope of the second trend, OmIs the mean slope, K, of the first trend slope and the second trend slopem,sdIs the standard deviation of the first trend slope and the second trend slope.
Preferably, the step of outputting corresponding warning information according to the attribute information of the extreme point of the monitoring category includes:
when the attribute information of the extreme point of the monitoring type is a maximum value, outputting warning information exceeding the upper limit of a threshold value;
and when the attribute information of the extreme point of the monitoring category is a minimum value, outputting warning information lower than a lower limit of a threshold value.
An embodiment of the present application provides a data trend analysis system, including:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring time sequence data of a monitored object, the monitored object comprises one or more monitoring categories, and the time sequence data is a parameter data set of each monitoring category on different time nodes;
the generating module is used for generating a trend graph corresponding to each monitoring category according to the time sequence data;
the statistical module is used for obtaining extreme points contained in each trend graph through statistics of a preset trend analysis algorithm;
the judging module is used for judging whether the monitoring category corresponding to the trend graph is abnormal or not according to the extreme point of each trend graph; and
and the output module is used for outputting corresponding warning information according to the attribute information of the extreme point of the monitoring type when the monitoring type is judged to be abnormal.
An embodiment of the present application provides a computer device, which includes a processor and a memory, wherein the memory stores a plurality of computer programs, and the processor is configured to implement the steps of the data trend analysis method as described above when executing the computer programs stored in the memory.
An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the data trend analysis method as described above.
According to the data trend analysis method, the data trend analysis system, the computer device and the computer readable storage medium, the trend graph is drawn by acquiring the time sequence data of one or more monitoring objects, trend analysis and state prediction are carried out on each monitoring type of the monitoring objects according to the trend graph, and corresponding warning information can be output.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart illustrating the steps of a data trend analysis method according to an embodiment of the present invention.
FIG. 2 is a functional block diagram of a data trend analysis system according to an embodiment of the present invention.
FIG. 3 is a schematic diagram of a computer device according to an embodiment of the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments thereof which are illustrated in the appended drawings. In addition, the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth to provide a thorough understanding of the present invention, and the described embodiments are merely a subset of the embodiments of the present invention, rather than a complete embodiment. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
Preferably, the data trend analysis method of the present invention is applied in one or more computer devices. The computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The computer device may be a desktop computer, a notebook computer, a tablet computer, a server, or other computing equipment. The computer device can be in man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.
The first embodiment is as follows:
FIG. 1 is a flow chart of the steps of a preferred embodiment of the data trend analysis method of the present invention. The order of the steps in the flow chart may be changed and some steps may be omitted according to different needs.
Referring to fig. 1, the data trend analysis method specifically includes the following steps.
Step S11, obtaining time series data of a monitored object, where the monitored object includes one or more monitoring categories, and the time series data is a parameter data set of each monitoring category at different time nodes.
In one embodiment, the monitoring object may be connected to one or more monitoring objects through an access network, so as to obtain the time-series data of the monitoring object. The monitoring object may be a server, a server cluster or other electronic devices. The server or the cluster of servers may include a number of hardware resources (e.g., CPU, memory, I/O interfaces, storage, etc.). The server or the server cluster may run different or the same operating system, database, application software, system software. It is understood that the server cluster may be composed of a plurality of Virtual Machine Managers (VMMs) running thereon, and a plurality of Physical Nodes (PNs), where the VMMs run thereon a plurality of operating systems, and the operating systems share the resources of the Physical machines through a resource scheduling algorithm of the VMMs.
The monitoring object may include one or more monitoring categories. When the monitoring object is monitored, the parameter data of the corresponding type index output by each monitoring type can be obtained. It is understood that the time-series data of the monitoring object is a parameter data set of corresponding type indexes output by a plurality of monitoring categories at different time nodes.
In one embodiment, the monitoring object may include a system resource object and/or a service type object, and the time series data of the monitoring object may be received/acquired in real time or periodically. For example, the time-series data is read from the monitoring object at every preset time, or the time-series data is uploaded to a data trend analysis system by the monitoring object at every preset time.
For example, when a server is used as a monitoring object of a system resource, the monitoring object may include monitoring categories of hardware classes such as a CPU, a memory, and a hard disk, and may further include monitoring categories of software classes such as a database and system software running in the server. When the monitoring type is CPU, the parameter data of the type indexes such as utilization rate (percentage of time for the processor to execute non-idle threads), interrupt rate (the number of times for the processor to interrupt the processor per second), system call rate (comprehensive rate for the processor to call the service routine of the operating system) and the like can be output; when the monitoring type is the memory, parameter data of type indexes such as page missing rate (representing that a processor requests a page from a specified position of the memory to have errors) and the like can be output; when the monitoring type is the hard disk, parameter data of type indexes such as the average number of read and write requests (the hard disk is queued in an example interval) can be output; when the monitoring type is a database, parameter data of type indexes such as data read-write performance and the like can be output.
When the service type is used as a monitoring object of the service class, it may include monitoring categories such as user login amount, user registration amount, core transaction data, and the like. For example, when the monitoring type is the user login amount, parameter data of type indexes such as the user online amount and the like can be output; when the monitoring type is user registration amount, parameter data of type indexes such as the number of registered account numbers and the like can be output, and when the monitoring type is core transaction data, parameter data of type indexes such as orders, click advertisements and the like can be output.
It is understood that the time series data can be represented as parameter data v of the type index corresponding to the monitoring category at the time t. Thus, when a monitoring category includes a type indicator, the time-series data of the corresponding monitoring category can be represented as: { X ═ v1,t1),(v2,t2),...,(vn,tn) Wherein n is a natural number, (v)n,tn) Representing a time node tnTime node data of tn>tn-1I.e. time node data (v)n,tn) The latest time node data; when the monitoring category includes two or more type indexes, the time-series data of the monitoring category can be represented as: x ═ X1,X2,…,XmIn which XmCan be expressed as: { Xm＝(v1m,t1),(v2m,t2),…,(vnm,tn) Where m denotes the number of type indices and n is a natural number.
In one embodiment, after the time-series data of the monitored object is obtained, the time-series data can be locally stored, so that the data can be analyzed and read later. The time series data may be stored in a relational database by default, that is, the parameter data v of the time t and the type index in the time series data is stored in the relational database as a key value pair. The relational database can be an RRD Tool database directly based on simple storage of files, an openntsdb database constructed based on a K/V database, and mysql and postgresql databases constructed based on the relational database.
In other embodiments of the present invention, when the requirement for data storage is high or the data size is large, the time series data may be stored in the time series data database, so as to improve the data reading and writing efficiency and reduce the storage space occupied by the data. The time series data database can comprise a search engine elastic search, Crate. io, Solr database constructed based on Lucene, or a Vertica, Actian database based on a column-wise storage database.
And step S12, generating a trend graph corresponding to each monitoring category according to the time series data.
In one embodiment, a trend graph corresponding to the monitored object can be generated according to the time series data so as to intuitively know the state of the monitored object.
When the monitoring category has a type indicator (e.g., when the monitoring category is a memory, it has a type indicator of page missing rate, or when the monitoring category is a user login amount, it has a type indicator of user online amount), the corresponding time series data can be expressed as { X ═ (v ═ v-1,t1),(v2,t2),…,(vn,tn) Establishing an XY coordinate axis, and setting each time t in the time sequence datanParameter data v of a type index corresponding to a point on the horizontal axis (X-axis) of the trend graphnAnd as the value on the vertical axis (Y axis) in the trend graph, connecting the parameter information of the corresponding type index through a straight line or a smooth curve, thus generating the trend graph corresponding to the monitoring class.
When a monitoring category has two or more type indices, the corresponding time series data can be represented as: x ═ X1,X2,…,XmTherein of，XmCan be expressed as: { Xm＝(v1m,t1),(v2m,t2),…,(vnm,tn)}. For example, when the monitoring category is CPU, it has three types of indicators, i.e. utilization rate, interrupt rate and system call rate, and at this time, the acquired time series data can be represented as X ═ X1,X2,X3Classifying and splitting the time series data to obtain a sub-time series X corresponding to each type of index1、X2、X3Wherein X is1Type index, X corresponding to utilization2Type index, X, corresponding to interrupt rate3And drawing a trend graph corresponding to each type index according to the mode corresponding to the type index of the system call rate.
For example, for a trend graph of CPU utilization, for X1For a sub-time sequence, it can be expressed as { X }1＝(v11,t1),(v12,t2),…,(vn1,tn) All the time t in the time sequence data can be converted intonParameter information v of the corresponding type index as a point on the horizontal axis of the first tendency chartn1And as the value on the vertical axis in the first trend graph, connecting the parameter information of the corresponding type index through a straight line or a smooth curve, thus generating the first trend graph corresponding to the CPU utilization rate. Similarly, for a trend graph of CPU interrupt rate, for X2For a sub-time sequence, it can be expressed as { X }2＝(v21,t1),(v22,t2),…,(vn2,tn) All the time t in the time sequence data can be converted intonParameter information v of the corresponding type index as a point on the horizontal axis of the trend graphn2And as the value on the vertical axis in the trend graph, connecting the parameter information of the corresponding type index through a straight line or a smooth curve, thus generating a second trend graph corresponding to the CPU interrupt rate. Thus, the trend graph with the monitoring type being the CPU can comprise the trend graphs respectively corresponding to the three types of indexes of the utilization rate, the interrupt rate and the system call.
And step S13, obtaining the extreme points contained in each trend graph through statistics of a preset trend analysis algorithm.
In one embodiment, the extreme points included in each of the trend graphs can be statistically derived by: randomly selecting time node data and previous time node data adjacent to the time node data from a trend graph, calculating a trend slope between the time node data and the previous time node data, and judging whether the calculated trend slope is greater than a preset threshold value; and when the trend slope is larger than the preset threshold, judging that the time node data is an extreme point in the trend graph.
For example, a time node data (v) is selected from a trend graphm,tm) And last time node data (v) adjacent to said time node datam-1,tm-1) The trend slope can be calculated by the following mathematical formula:
Km＝|(Vm-Vm-1)/(tm-tm-1)|
wherein, KmIs a trend slope. If the trend slope Km>R, wherein R represents a preset threshold, the time node data (v) may be determinedm,tm) Is an extreme point in the trend graph.
In one embodiment, the set of all extreme points in a trend graph can be represented as an extreme set. The R values for different category indices may be set differently. For example, depending on the application, it is preferable that the utilization of the CPU fluctuates within ± 5%. If the CPU utilization rate is too low, the CPU utilization rate of the server is not high; too high, the CPU may become a processing bottleneck for the system. Thus, for a monitoring class that is a CPU, the preset threshold for the type indicator of its utilization may be set to [ -5,5 ]. For the interrupt rate of the CPU, in general, the lower the processor interrupt rate, the better; not more than 1000 times/second; if the value of the interrupt rate increases significantly, indicating that there may be a hardware problem, it may be necessary to check the network adapter, disk, or other hardware that caused the interrupt. Thus, for the monitoring class CPU, the preset threshold value for the type indication of the interrupt rate is 1000 times.
Step S14, determining whether the monitoring category corresponding to the trend graph is abnormal according to the extreme point of each trend graph.
In one embodiment, the comprehensive trend slope K corresponding to two or more time series data adjacent to the extreme point in a trend graph can be used to determine whether the monitoring category corresponding to the trend graph is abnormal. Specifically, the method comprises the following steps: firstly, randomly selecting an extreme point from a trend graph, and acquiring at least two previous time node data adjacent to the extreme point; secondly, respectively calculating a first trend slope between the extreme value point and first time node data and a second trend slope between the extreme value point and second time node data, wherein the first time node data is the last time node data adjacent to the extreme value point, and the second time node data is the last time node data adjacent to the first time node data; then, calculating the standard deviation and the mean slope of the first trend slope and the second trend slope; thirdly, calculating to obtain the comprehensive trend slope of the extreme point according to the standard deviation and the mean slope obtained by calculation; finally, judging whether the comprehensive trend slope of the extreme point is within a preset range value; and when the comprehensive trend slope of the extreme point is not within the preset range value, judging that the monitoring category corresponding to the trend graph is abnormal.
For example, for an extreme point, the corresponding time series data is (v)m,tm) Thus, the two time series data adjacent to the extreme point are respectively (v)m-1,tm-1)、(vm-2,tm-2) (ii) a The three time series data adjacent to the extreme point are respectively (v)m-1,tm-1)、(vm-2,tm-2)、(vm-3,tm-3). The following example takes the extreme point and the three previous time node data adjacent to the extreme point as an example:
suppose, time series data (v)m,tm) And time series data (v)m-1,tm-1) The gradient of the trend therebetween is Km,m-1(ii) a Time series data (v)m,tm) And time series data (v)m-2,tm-2) The gradient of the trend therebetween is Km,m-2Time series data (v)m,tm) And time series data (v)m-3,tm-3) The gradient of the trend therebetween is Km,m-3；
Slope of the trend Km,m-1、Km,m-2、Km,m-3Standard deviation K betweenm,sdCan be calculated by the following mathematical formula:
slope of the trend Km,m-1、Km,m-2、Km,m-3Mean slope ofmCan be calculated by the following mathematical formula: o ism＝(Km,m-1+Km,m-2+Km,m-2)/3；
For extreme point (v)m,tm) The overall trend slope K of (a) can be calculated by the following mathematical formula:
K＝(Km,m-1-Om)/Km,sd*Km,m-1+(Km,m-2-Om)/Km,sd*Km,m-2+(Km,m-3-Om)/Km,sd*Km,m-3；
judging whether the comprehensive trend slope K is in a preset range [ -c, c ], and if the comprehensive trend slope K is in the preset range [ -c, c ], indicating that the state of the monitoring category is normal; and when K is not in the preset range < -c, c >, the state of the monitoring class is abnormal. The preset range [ -c, c ] can be set and adjusted according to actual use requirements.
And step S15, when the monitoring type is judged to be abnormal, outputting corresponding warning information according to the attribute information of the extreme point of the monitoring type.
In an embodiment, the extreme points in the extreme value set may include several maximum values, minimum values, that is, the attribute information of the extreme points may include a maximum value or a minimum value. Specifically, two adjacent time series data (v)m,tm)、(vm-1,tm-1) When the trend slope Kn＝(vm-vm-1)/(tm-tm-1)>R is time series data (v)m,tm) The correspondence is a maximum value; current trend slope Kn＝(vm-vm-1)/(tm-tm-1)<-R time, representing time series data (v)m,tm) Corresponding to a minimum value. When the state of the monitoring type is abnormal, if the attribute information of the corresponding extreme point is a maximum value, warning information exceeding the upper limit of the threshold value can be output; if the attribute information of the corresponding extreme point is a minimum value, warning information lower than a lower limit of a threshold value can be output.
Example two:
FIG. 2 is a functional block diagram of a data trend analysis system according to a preferred embodiment of the present invention.
Referring to fig. 2, the data trend analysis system 10 may include an obtaining module 101, a generating module 102, a counting module 103, a determining module 104, and an outputting module 105.
The obtaining module 101 is configured to obtain time-series data of a monitored object, where the monitored object includes one or more monitoring categories, and the time-series data is a parameter data set of each monitoring category at different time nodes.
In an embodiment, the obtaining module 101 may connect to one or more monitoring objects through an access network to obtain the time-series data of the monitoring objects. The monitoring object may be a server, a server cluster or other electronic devices. The server or the cluster of servers may include a number of hardware resources (e.g., CPU, memory, I/O interfaces, storage, etc.). The server or the server cluster may run different or the same operating system, database, application software, system software. It is understood that the server cluster may be composed of a plurality of Virtual Machine Managers (VMMs) running thereon, and a plurality of Physical Nodes (PNs), where the VMMs run thereon a plurality of operating systems, and the operating systems share the resources of the Physical machines through a resource scheduling algorithm of the VMMs.
The monitoring object may include one or more monitoring categories. When the monitoring object is monitored, the parameter data of the corresponding type index output by each monitoring type can be obtained. It is understood that the time-series data of the monitoring object is a parameter data set of corresponding type indexes output by a plurality of monitoring categories at different time nodes.
In one embodiment, the monitoring object may include a system resource object and/or a service type object, and the time series data of the monitoring object may be received/acquired in real time or periodically. For example, the time-series data is read from the monitoring object at every preset time, or the time-series data is uploaded to a data trend analysis system by the monitoring object at every preset time.
For example, when a server is used as a monitoring object of a system resource, the monitoring object may include monitoring categories of hardware classes such as a CPU, a memory, and a hard disk, and may further include monitoring categories of software classes such as a database and system software running in the server. When the monitoring type is CPU, the parameter data of the type indexes such as utilization rate (percentage of time for the processor to execute non-idle threads), interrupt rate (the number of times for the processor to interrupt the processor per second), system call rate (comprehensive rate for the processor to call the service routine of the operating system) and the like can be output; when the monitoring type is the memory, parameter data of type indexes such as page missing rate (representing that a processor requests a page from a specified position of the memory to have errors) and the like can be output; when the monitoring type is the hard disk, parameter data of type indexes such as the average number of read and write requests (the hard disk is queued in an example interval) can be output; when the monitoring type is a database, parameter data of type indexes such as data read-write performance and the like can be output.
When the service type is used as a monitoring object of the service class, it may include monitoring categories such as user login amount, user registration amount, core transaction data, and the like. For example, when the monitoring type is the user login amount, parameter data of type indexes such as the user online amount and the like can be output; when the monitoring type is user registration amount, parameter data of type indexes such as the number of registered account numbers and the like can be output, and when the monitoring type is core transaction data, parameter data of type indexes such as orders, click advertisements and the like can be output.
It is understood that the time series data can be represented as parameter data v of the type index corresponding to the monitoring category at the time t. Thus, when a monitoring category includes a type indicator, the time-series data of the corresponding monitoring category can be represented as: { X ═ v1,t1),(v2,t2),...,(vn,tn) Wherein n is a natural number, (v)n,tn) Representing a time node tnTime node data of tn>tn-1I.e. time node data (v)n,tn) The latest time node data; when the monitoring category includes two or more type indexes, the time-series data of the monitoring category can be represented as: x ═ X1,X2,…,XmIn which XmCan be expressed as: { Xm＝(v1m,t1),(v2m,t2),…,(vnm,tn) Where m denotes the number of type indices and n is a natural number.
In an embodiment, after the obtaining module 101 obtains the time-series data of the monitored object, it can also perform local storage on the time-series data, so as to facilitate subsequent data analysis and reading. The time series data may be stored in a relational database by default, that is, the parameter data v of the time t and the type index in the time series data is stored in the relational database as a key value pair. The relational database can be an RRD Tool database directly based on simple storage of files, an openntsdb database constructed based on a K/V database, and mysql and postgresql databases constructed based on the relational database.
In other embodiments of the present invention, when the requirement for data storage is high or the data size is large, the time series data may be stored in the time series data database, so as to improve the data reading and writing efficiency and reduce the storage space occupied by the data. The time series data database can comprise a search engine elastic search, Crate. io, Solr database constructed based on Lucene, or a Vertica, Actian database based on a column-wise storage database.
The generating module 102 is configured to generate a trend graph corresponding to each of the monitoring categories according to the time-series data.
In one embodiment, the generating module 102 may generate a trend graph corresponding to the monitoring object according to the time series data to intuitively know the state of the monitoring object.
When the monitoring category has a type indicator (e.g., when the monitoring category is a memory, it has a type indicator of page missing rate, or when the monitoring category is a user login amount, it has a type indicator of user online amount), the corresponding time series data can be expressed as { X ═ (v ═ v-1,t1),(v2,t2),…,(vn,tn) Establishing an XY coordinate axis, and setting each time t in the time sequence datanParameter data v of a type index corresponding to a point on the horizontal axis (X-axis) of the trend graphnAnd as the value on the vertical axis (Y axis) in the trend graph, connecting the parameter information of the corresponding type index through a straight line or a smooth curve, thus generating the trend graph corresponding to the monitoring class.
When a monitoring category has two or more type indices, the corresponding time series data can be represented as: x ═ X1,X2,…,XmIn which XmCan be expressed as: { Xm＝(v1m,t1),(v2m,t2),…,(vnm,tn)}. For example, when the monitoring category is CPU, it has three types of indicators, i.e. utilization rate, interrupt rate and system call rate, and at this time, the acquired time series data can be represented as X ═ X1,X2,X3Classifying and splitting the time series data to obtain a sub-time series X corresponding to each type of index1、X2、X3Wherein X is1Type index, X corresponding to utilization2Type index, X, corresponding to interrupt rate3And drawing a trend graph corresponding to each type index according to the mode corresponding to the type index of the system call rate.
For example, for a trend graph of CPU utilization, for X1For a sub-time sequence, it can be expressed as { X }1＝(v11,t1),(v12,t2),…,(vn1,tn) All the time t in the time sequence data can be converted intonParameter information v of the corresponding type index as a point on the horizontal axis of the first tendency chartn1As the value on the vertical axis in the first trend graph, the parameter information of the corresponding type index is connected by a straight line or a smooth curve, so that the generating module 102 can generate the first trend graph corresponding to the CPU utilization. Similarly, for a trend graph of CPU interrupt rate, for X2For a sub-time sequence, it can be expressed as { X }2＝(v21,t1),(v22,t2),…,(vn2,tn) All the time t in the time sequence data can be converted intonParameter information v of the corresponding type index as a point on the horizontal axis of the trend graphn2As the values on the vertical axis in the trend graph, the parameter information of the corresponding type index is connected by a straight line or a smooth curve, so that the generating module 102 can generate a second trend graph corresponding to the CPU interrupt rate. Thus, the trend graph with the monitoring type being the CPU can comprise the trend graphs respectively corresponding to the three types of indexes of the utilization rate, the interrupt rate and the system call.
The statistical module 103 is configured to statistically obtain extreme points included in each of the trend graphs through a preset trend analysis algorithm.
In one embodiment, the statistical module 103 may statistically derive the extreme points included in each of the trend graphs by: randomly selecting time node data and previous time node data adjacent to the time node data from a trend graph, calculating a trend slope between the time node data and the previous time node data, and judging whether the calculated trend slope is greater than a preset threshold value; and when the trend slope is larger than the preset threshold, judging that the time node data is an extreme point in the trend graph.
For example, a time node data (v) is selected from a trend graphm,tm) And last time node data (v) adjacent to said time node datam-1,tm-1) The trend slope can be calculated by the following mathematical formula:
Km＝|(Vm-Vm-1)/(tm-tm-1)|
wherein, KmIs a trend slope. If the trend slope Km>R, wherein R represents a preset threshold, the time node data (v) may be determinedm,tm) Is an extreme point in the trend graph.
In one embodiment, the set of all extreme points in a trend graph can be represented as an extreme set. The R values for different category indices may be set differently. For example, depending on the application, it is preferable that the utilization of the CPU fluctuates within ± 5%. If the CPU utilization rate is too low, the CPU utilization rate of the server is not high; too high, the CPU may become a processing bottleneck for the system. Thus, for a monitoring class that is a CPU, the preset threshold for the type indicator of its utilization may be set to [ -5,5 ]. For the interrupt rate of the CPU, in general, the lower the processor interrupt rate, the better; not more than 1000 times/second; if the value of the interrupt rate increases significantly, indicating that there may be a hardware problem, it may be necessary to check the network adapter, disk, or other hardware that caused the interrupt. Thus, for the monitoring class CPU, the preset threshold value for the type indication of the interrupt rate is 1000 times.
The determining module 104 is configured to determine whether the monitoring category corresponding to each of the trend graphs is abnormal according to the extreme point of the trend graph.
In one embodiment, the determining module 104 is configured to determine whether the monitoring category corresponding to a trend graph is abnormal according to a comprehensive trend slope K corresponding to two or more time series data adjacent to the extreme point in the trend graph. Specifically, the method comprises the following steps: firstly, randomly selecting an extreme point from a trend graph, and acquiring at least two previous time node data adjacent to the extreme point; secondly, respectively calculating a first trend slope between the extreme value point and first time node data and a second trend slope between the extreme value point and second time node data, wherein the first time node data is the last time node data adjacent to the extreme value point, and the second time node data is the last time node data adjacent to the first time node data; then, calculating the standard deviation and the mean slope of the first trend slope and the second trend slope; thirdly, calculating to obtain the comprehensive trend slope of the extreme point according to the standard deviation and the mean slope obtained by calculation; finally, judging whether the comprehensive trend slope of the extreme point is within a preset range value; and when the comprehensive trend slope of the extreme point is not within the preset range value, judging that the monitoring category corresponding to the trend graph is abnormal.
For example, for an extreme point, the corresponding time series data is (v)m,tm) Thus, the two time series data adjacent to the extreme point are respectively (v)m-1,tm-1)、(vm-2,tm-2) (ii) a The three time series data adjacent to the extreme point are respectively (v)m-1,tm-1)、(vm-2,tm-2)、(vm-3,tm-3). The following example takes the extreme point and the three previous time node data adjacent to the extreme point as an example:
suppose, time series data (v)m,tm) And time series data (v)m-1,tm-1) The gradient of the trend therebetween is Km,m-1(ii) a Time series data (v)m,tm) And time series data (v)m-2,tm-2) The gradient of the trend therebetween is Km,m-2Time series data (v)m,tm) And time series data (v)m-3,tm-3) The gradient of the trend therebetween is Km,m-3；
Slope of the trend Km,m-1、Km,m-2、Km,m-3Standard deviation K betweenm,sdCan be calculated by the following mathematical formula:
slope of the trend Km,m-1、Km,m-2、Km,m-3Mean slope ofmCan be calculated by the following mathematical formula: o ism＝(Km,m-1+Km,m-2+Km,m-2)/3；
For extreme point (v)m,tm) The overall trend slope K of (a) can be calculated by the following mathematical formula:
K＝(Km,m-1-Om)/Km,sd*Km,m-1+(Km,m-2-Om)/Km,sd*Km,m-2+(Km,m-3-Om)/Km,sd*Km,m-3；
judging whether the comprehensive trend slope K is in a preset range [ -c, c ], and if the comprehensive trend slope K is in the preset range [ -c, c ], indicating that the state of the monitoring category is normal; and when K is not in the preset range < -c, c >, the state of the monitoring class is abnormal. The preset range [ -c, c ] can be set and adjusted according to actual use requirements.
The output module 105 is configured to output corresponding warning information according to the attribute information of the extreme point of the monitoring category when the monitoring category is determined to be abnormal.
In an embodiment, the extreme points in the extreme value set may include several maximum values, minimum values, that is, the attribute information of the extreme points may include a maximum value or a minimum value. Specifically, two adjacent time series data (v)m,tm)、(vm-1,tm-1) When the trend slope Kn＝(vm-vm-1)/(tm-tm-1)>R is time series data (v)m,tm) The correspondence is a maximum value; current trend slope Kn＝(vm-vm-1)/(tm-tm-1)<-R time, representing time series data (v)m,tm) Corresponding to a minimum value. When the state of the monitoring category is abnormal, if the attribute information of the corresponding extreme point is a maximum value, the output module 105 may output warning information exceeding the upper limit of the threshold; if the attribute information of the corresponding extreme point is a minimum value, the output moduleBlock 105 may output alert information below a lower threshold limit.
FIG. 3 is a diagram of a computer device according to a preferred embodiment of the present invention.
The computer device 1 comprises a memory 20, a processor 30 and a computer program 40, such as a data trend analysis program, stored in the memory 20 and executable on the processor 30. The processor 30, when executing the computer program 40, implements the steps of the data trend analysis method embodiments described above, such as the steps S11-S15 shown in fig. 1. Alternatively, the processor 30, when executing the computer program 40, implements the functions of the modules in the data trend analysis system embodiments, such as the modules 101-105 in fig. 2.
Illustratively, the computer program 40 may be partitioned into one or more modules/units that are stored in the memory 20 and executed by the processor 30 to implement the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, the instruction segments describing the execution process of the computer program 40 in the computer apparatus 1. For example, the computer program 40 may be divided into the acquiring module 101, the generating module 102, the counting module 103, the judging module 104, and the outputting module 105 in fig. 2. See embodiment two for specific functions of each module.
The computer device 1 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. It will be appreciated by a person skilled in the art that the schematic diagram is merely an example of the computer apparatus 1, and does not constitute a limitation of the computer apparatus 1, and may comprise more or less components than those shown, or some components may be combined, or different components, for example, the computer apparatus 1 may further comprise an input and output device, a network access device, a bus, etc.
The Processor 30 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general purpose processor may be a microprocessor or the processor 30 may be any conventional processor or the like, the processor 30 being the control center of the computer device 1, various interfaces and lines connecting the various parts of the overall computer device 1.
The memory 20 may be used for storing the computer program 40 and/or the module/unit, and the processor 30 implements various functions of the computer device 1 by running or executing the computer program and/or the module/unit stored in the memory 20 and calling data stored in the memory 20. The memory 20 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the computer apparatus 1, and the like. In addition, the memory 20 may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The modules/units integrated with the computer device 1 may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and which, when executed by a processor, may implement the steps of the above-described embodiments of the method. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
In the embodiments provided in the present invention, it should be understood that the disclosed computer apparatus and method can be implemented in other ways. For example, the above-described embodiments of the computer apparatus are merely illustrative, and for example, the division of the units is only one logical function division, and there may be other divisions when the actual implementation is performed.
In addition, functional units in the embodiments of the present invention may be integrated into the same processing unit, or each unit may exist alone physically, or two or more units are integrated into the same unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. The units or computer means recited in the computer means claims may also be implemented by the same unit or computer means, either in software or in hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (9)
1. A method of data trend analysis, the method comprising:
acquiring time sequence data of a monitored object, wherein the monitored object comprises one or more monitoring categories, and the time sequence data is a parameter data set of each monitoring category on different time nodes;
generating a trend graph corresponding to each monitoring category according to the time sequence data;
obtaining extreme points contained in each trend graph through statistics of a preset trend analysis algorithm, wherein when the trend slope between a time node data in a certain trend graph and a last time node data adjacent to the time node data is larger than a preset threshold value, the time node data is judged to be the extreme points of the trend graph;
judging whether the monitoring type corresponding to each trend graph is abnormal or not according to the extreme point of each trend graph; and
when the monitoring type is judged to be abnormal, outputting corresponding warning information according to the attribute information of the extreme point of the monitoring type;
the step of judging whether the monitoring category corresponding to the trend graph is abnormal or not according to the extreme point of each trend graph comprises the following steps:
randomly selecting an extreme point from a certain trend graph, and acquiring at least two pieces of previous time node data adjacent to the extreme point, wherein the at least two pieces of previous time node data comprise first time node data and second time node data;
respectively calculating a first trend slope between the extreme point and the first time node data, and a second trend slope between the extreme point and the second time node data, wherein the first time node data is the last time node data adjacent to the extreme point, and the second time node data is the last time node data adjacent to the first time node data;
calculating to obtain a comprehensive trend slope of the extreme point based on the first trend slope and the second trend slope;
judging whether the comprehensive trend slope of the extreme point is within a preset range value or not; and
and when the comprehensive trend slope of the extreme point is not within the preset range value, judging that the monitoring type corresponding to the trend graph is abnormal.
2. The data trend analysis method of claim 1, wherein the step of generating a trend graph corresponding to each of the monitoring categories from the time-series data comprises:
classifying the time series data according to each monitoring category;
establishing an XY coordinate axis, and taking each time node in the time sequence data of the first monitoring category of the monitored object as a point of the trend graph on the X axis; and
and taking the parameter data corresponding to each time node as the value of the trend graph on the Y axis to obtain the trend graph corresponding to the first monitoring category.
3. The data trend analysis method of claim 1, wherein the trend slope of the time node data adjacent to the time node data at the previous time node data is calculated by the following mathematical formula:
Km＝|(Vm-Vm-1)/(tm-tm-1)|；
wherein, KmIs the slope of the trend, tmIs a time node corresponding to the time node data, tm-1Is given asmAdjacent last time node, VmAs time node tmCorresponding parameter data, Vm-1As time node tm-1Corresponding parameter data.
4. The data trend analysis method of claim 1, wherein the step of calculating a composite trend slope for the extreme point based on the first trend slope and the second trend slope comprises:
calculating the standard deviation and the mean slope of the first trend slope and the second trend slope;
and calculating to obtain the comprehensive trend slope of the extreme point according to the first trend slope, the second trend slope, the standard deviation and the mean slope.
5. The data trend analysis method of claim 4, wherein the integrated trend slope of the extreme points is calculated by the following mathematical formula:
K＝(Km,m-1-Om)/Km,sd*Km,m-1+(Km,m-2-Om)/Km,sd*Km,m-2；
wherein K is the slope of the comprehensive trend, Km,m-1Is the first trend slope, Km,m-2Is the slope of the second trend, OmIs the mean slope, K, of the first trend slope and the second trend slopem,sdIs the standard deviation of the first trend slope and the second trend slope.
6. The data trend analysis method of claim 3, wherein the step of outputting corresponding warning information according to the attribute information of the extreme point of the monitoring category comprises:
when the attribute information of the extreme point of the monitoring category is a maximum value, outputting warning information exceeding the upper limit of a threshold value, wherein the attribute information of the extreme point is that the maximum value meets the following relational expression: km＞R，KmThe trend slope is shown, and R is the preset threshold value;
when the attribute information of the extreme point of the monitoring category is a minimum value, outputting warning information lower than a lower limit of a threshold value, wherein the attribute information of the extreme point is that the minimum value meets the following relational expression: km＜-R。
7. A data trend analysis system, the system comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring time sequence data of a monitored object, the monitored object comprises one or more monitoring categories, and the time sequence data is a parameter data set of each monitoring category on different time nodes;
the generating module is used for generating a trend graph corresponding to each monitoring category according to the time sequence data;
the statistical module is used for obtaining the extreme points contained in each trend graph through the statistics of a preset trend analysis algorithm, wherein when the trend slope between a time node data in a certain trend graph and the last time node data adjacent to the time node data is larger than a preset threshold value, the time node data is judged to be the extreme points of the trend graph;
the system comprises a judging module, a calculating module and a processing module, wherein the judging module is used for randomly selecting an extreme point from a certain trend graph and acquiring at least two pieces of previous time node data adjacent to the extreme point, and the at least two pieces of previous time node data comprise first time node data and second time node data;
the judgment module is further used for respectively calculating a first trend slope between the extreme point and the first time node data and a second trend slope between the extreme point and the second time node data, wherein the first time node data is the last time node data adjacent to the extreme point, and the second time node data is the last time node data adjacent to the first time node data;
the judgment module is further used for calculating to obtain a comprehensive trend slope of the extreme point based on the first trend slope and the second trend slope, judging whether the comprehensive trend slope of the extreme point is within a preset range value, and judging that the monitoring type corresponding to the trend graph is abnormal when the comprehensive trend slope of the extreme point is not within the preset range value; and
and the output module is used for outputting corresponding warning information according to the attribute information of the extreme point of the monitoring type when the monitoring type is judged to be abnormal.
8. A computer arrangement comprising a processor and a memory, the memory having stored thereon a number of computer programs, wherein the processor is adapted to carry out the steps of the data trend analysis method according to any one of claims 1-6 when executing the computer programs stored in the memory.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the data trend analysis method according to any one of claims 1 to 6.
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Families Citing this family (27)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111984827A (en) * | 2019-05-24 | 2020-11-24 | 上海东方富联科技有限公司 | Door sensor data anomaly detection method and system, storage medium and terminal |
| CN114041122A (en) * | 2019-07-09 | 2022-02-11 | 华为技术有限公司 | Object representation index display method and device |
| CN110659178B (en) * | 2019-08-30 | 2023-03-28 | 中国人民财产保险股份有限公司 | System running state adjusting method and device, storage medium and electronic equipment |
| CN110782989B (en) * | 2019-09-18 | 2022-06-17 | 平安科技（深圳）有限公司 | Data analysis method, device, equipment and computer readable storage medium |
| CN110795312A (en) * | 2019-10-30 | 2020-02-14 | 邵忠 | Interface display method and device |
| CN111931969A (en) * | 2020-03-20 | 2020-11-13 | 中国南方电网有限责任公司超高压输电公司广州局 | Merging unit equipment state prediction method based on time sequence analysis |
| JP7454419B2 (en) * | 2020-03-25 | 2024-03-22 | 三菱重工業株式会社 | Plant operation data monitoring device and method |
| CN111651503B (en) * | 2020-06-03 | 2022-12-23 | 南方电网科学研究院有限责任公司 | Power distribution network data anomaly identification method and system and terminal equipment |
| CN111797286B (en) * | 2020-07-07 | 2024-04-05 | 国家电网有限公司大数据中心 | Time zone determining method, device, equipment and medium |
| CN112199258A (en) * | 2020-11-13 | 2021-01-08 | 新华三大数据技术有限公司 | Method and device for monitoring magnetic disk, electronic equipment and medium |
| CN112486773A (en) * | 2020-12-01 | 2021-03-12 | 北京数码视讯技术有限公司 | Data processing method, device and system |
| CN112527598B (en) * | 2020-12-10 | 2023-08-18 | 北京百度网讯科技有限公司 | Method, apparatus, device, storage medium and program product for monitoring data |
| CN113280854A (en) * | 2021-04-02 | 2021-08-20 | 无锡先导智能装备股份有限公司 | Monitoring method and device for induction heating device, computer equipment and storage medium |
| CN113253014B (en) * | 2021-04-07 | 2022-08-23 | 国网河北省电力有限公司衡水供电分公司 | Method, device and equipment for detecting abnormal topological relation of transformer area subscriber |
| CN113220526A (en) * | 2021-05-06 | 2021-08-06 | 国家计算机网络与信息安全管理中心 | Method and device for detecting family scale abnormality of botnet |
| CN113220551A (en) * | 2021-05-12 | 2021-08-06 | 康键信息技术（深圳）有限公司 | Index trend prediction and early warning method and device, electronic equipment and storage medium |
| CN113360720B (en) * | 2021-06-24 | 2023-11-21 | 湖北华中电力科技开发有限责任公司 | Data asset visualization method, device and equipment based on data blood relationship |
| CN113535516B (en) * | 2021-07-06 | 2023-05-30 | 上海上讯信息技术股份有限公司 | Method and equipment for monitoring CPU occupancy rate abnormality |
| CN113657622B (en) * | 2021-07-13 | 2024-02-27 | 国网河北省电力有限公司电力科学研究院 | Multi-dimensional state data fusion method, device, terminal and storage medium for power equipment |
| CN113722186B (en) * | 2021-09-07 | 2023-10-27 | 北京奇艺世纪科技有限公司 | Abnormality detection method and device, electronic equipment and storage medium |
| CN113766333B (en) * | 2021-09-07 | 2023-08-11 | 北京爱奇艺科技有限公司 | Method and device for determining video heat value, electronic equipment and storage medium |
| CN114035990B (en) * | 2021-10-23 | 2024-04-12 | 南京航空航天大学 | Real-time anomaly detection method for time sequence data of Linux operating system |
| CN114842623A (en) * | 2022-03-28 | 2022-08-02 | 广州明珞装备股份有限公司 | Workshop alarm trend analysis method, system, equipment and storage medium |
| CN114414931B (en) * | 2022-03-29 | 2022-06-21 | 北京航天和兴科技股份有限公司 | Cable network monitoring and detecting system and detecting method thereof |
| CN115001877B (en) * | 2022-08-08 | 2022-12-09 | 北京宏数科技有限公司 | Big data-based information security operation and maintenance management system and method |
| CN115618076B (en) * | 2022-11-07 | 2023-04-18 | 山东省地质矿产勘查开发局第一地质大队（山东省第一地质矿产勘查院） | Hydrogeological data management method |
| CN116701731B (en) * | 2023-06-08 | 2024-04-05 | 费莱(浙江)科技有限公司 | Dynamic monitoring and early warning method for operation stability in life cycle of motor |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016027390A1 (en) * | 2014-08-21 | 2016-02-25 | 日本電気株式会社 | Slope monitoring system, device for slope safety analysis, method, and program |
| CN106960126A (en) * | 2017-03-28 | 2017-07-18 | 联想(北京)有限公司 | Data early warning method and equipment |
| CN108376299A (en) * | 2018-02-27 | 2018-08-07 | 深圳市智物联网络有限公司 | A kind of prediction technique and device of running trend of the equipment |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8708905B2 (en) * | 2009-06-12 | 2014-04-29 | General Electric Company | Method, device and computer product for EEG monitoring, analysis and display |
| US9996444B2 (en) * | 2014-06-25 | 2018-06-12 | Vmware, Inc. | Automated methods and systems for calculating hard thresholds |
| CN107705149A (en) * | 2017-09-22 | 2018-02-16 | 平安科技（深圳）有限公司 | Data method for real-time monitoring, device, terminal device and storage medium |
| CN107766299B (en) * | 2017-10-24 | 2021-05-18 | 携程旅游信息技术（上海）有限公司 | Data index abnormity monitoring method and system, storage medium and electronic equipment |
-
2018
- 2018-10-31 CN CN201811289826.9A patent/CN109634801B/en active Active
-
2019
- 2019-03-08 WO PCT/CN2019/077516 patent/WO2020087829A1/en active Application Filing
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016027390A1 (en) * | 2014-08-21 | 2016-02-25 | 日本電気株式会社 | Slope monitoring system, device for slope safety analysis, method, and program |
| CN106960126A (en) * | 2017-03-28 | 2017-07-18 | 联想(北京)有限公司 | Data early warning method and equipment |
| CN108376299A (en) * | 2018-02-27 | 2018-08-07 | 深圳市智物联网络有限公司 | A kind of prediction technique and device of running trend of the equipment |
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| CN109634801A (en) | 2019-04-16 |
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